القائمة
Multi-agent team coordination — task delegation, shared state, and inter-agent communication
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | multi-agent |
| description | Multi-agent team coordination — task delegation, shared state, and inter-agent communication |
Team coordination system (agents/team_coordinator.py) for multi-agent workloads
with capability matching and load-balanced task assignment.
from agents.team_coordinator import TeamCoordinator, Agent
tc = TeamCoordinator(team_id="dev-team")
tc.add_agent(Agent(agent_id="coder-1", name="Codex", capabilities=["code"], max_tasks=3))
tc.add_agent(Agent(agent_id="reviewer-1", name="ReviewBot", capabilities=["review"], max_tasks=2))
assigned = tc.assign_task("code") # picks least-loaded capable agent
python -m pytest tests/test_team_coordinator.py -v
Onboard a new client/company onto this platform's real Agentic OS: create the company record, scan its websites/repos, auto-provision specialist agents, activate its 24x7 agency runtime, and know exactly which "OS" building blocks (memory, integrations, dashboard) already exist versus which are roadmap gaps. ADAPTED FROM: a third-party giveaway skill ("agentic-os-installer" by Gennaro Santoro / Operations Heroes) that described a generic vault + Google-suite + skill-pack installer. That skill's product (Obsidian vault, Gmail/Calendar/ Drive wiring, "skill packs") does not exist in this repo and its promotional content (Skool community link) does not belong here. This is a clean-room rewrite that keeps the useful idea — "stand up a working agency OS for a client from a short checklist" — and maps every step to the real module that already implements it in this codebase, per CLAUDE.md architecture rules.
Agile sprint planning, velocity tracking, and burndown metrics for agent-managed projects
Initiative-level portfolio management with dependency tracking and milestone coordination
AI-assisted engineering impact analysis — productivity metrics and code quality insights
Cross-harness agent patterns — standardize agent execution across different coding assistants
Temporal context graph for agent memory — track entity relationships and state changes over time